This article proposes Styrene-Butadiene Rubber(SBR)and Chem-lite CR Powder(CCP)as a sustainable solution for dispersive clays,which cause infrastructure damage due to high sodium ions.Traditionally utilized stabilizer...This article proposes Styrene-Butadiene Rubber(SBR)and Chem-lite CR Powder(CCP)as a sustainable solution for dispersive clays,which cause infrastructure damage due to high sodium ions.Traditionally utilized stabilizers like lime/cement raise environmental concerns due to their high carbon footprints.Regarding this,SBR/CCP has been used in concrete technology for several functions;nevertheless,its effectiveness for stabilizing dispersive clay remains uncertain.Therefore,this study investigated how SBR/CCP improved sodium-rich dispersive soil's dispersion,index,mechanical characteristics,and associated mechanism.Multiple tests,including double hydrometer,cation analysis,compression strength(UCS),physio-chemical,Atterberg's limits,California Bearing Ratio(CBR),X-Ray diffraction(XRD),scanning electron microscopy(SEM),and energy dispersive X-Ray spectroscopy(EDS)were performed at different mixing ratios up to curing of 60-d.The results showed a significant reduction in dispersion(61.7%),sodium(38%),and plasticity(50.4%)with an optimal 1.5%SBR-3%CCP mix after 28-d,converting the clay to a non-dispersive type.UCS and soaked CBR improved by 283%and 579%,respectively.Micro analyses revealed soil enhancement through CCP's flocculation,ion exchange,and pozzolanic reactions,while SBR-coated particles and filled pores formed reticulated membrane systems.SBR/CCP offers a sustainable/eco-friendly alternative for stabilizing dispersive clays with a lower carbon footprint.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
Convective flow is a self-sustained flow with the effect of the temperature gradient.The density is non-uniform due to the variation of temperature.The effect of the magnetic flux plays a major role in convective flow...Convective flow is a self-sustained flow with the effect of the temperature gradient.The density is non-uniform due to the variation of temperature.The effect of the magnetic flux plays a major role in convective flow.The process of heat transfer is accompanied by a mass transfer process;for instance,condensation,evaporation,and chemical process.Due to the applications of the heat and mass transfer combined effects in a different field,the main aim of this paper is to do a comprehensive analysis of heat and mass transfer of MHD unsteady second-grade fluid in the presence of ramped boundary conditions near a porous surface.The dynamical analysis of heat transfer is based on classical differentiation with no memory effects.The non-dimensional form of the governing equations of the model is developed.These are solved by the classical integral(Laplace)transform technique/method with the convolution theorem and closed-form solutions are attained for temperature,concentration,and velocity.The physical aspects of distinct parameters are discussed via graph to see the influence on the fluid concentration,velocity,and temperature.Our results suggest that the velocity profile decrease by increasing the Prandtl number.The existence of a Prandtl number may reflect the control of the thickness of momentum and enlargement of thermal conductivity.Furthermore,to validate our results,some results are recovered from the literature.展开更多
BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicti...BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.展开更多
In current study,the numerical computations of Reiner–Rivlin nanofluid flow through a rotational disk under the influence of thermal radiation and Arrhenius activation energy is considered.For innovative physical sit...In current study,the numerical computations of Reiner–Rivlin nanofluid flow through a rotational disk under the influence of thermal radiation and Arrhenius activation energy is considered.For innovative physical situations,the motile microorganisms are incorporated too.The multiple slip effects are considered in the boundary conditions.The bioconvection of motile microorganism is utilized alongside nanofluids to provide stability to enhanced thermal transportation.The Bioconvection pattern in various nanoparticles accredits novel applications of biotechnology like the synthesis of biological polymers,biosensors,fuel cells,petroleum engineering,and the natural environment.By deploying some suitable similarity transformation functions,the governing partial differential equations(PDEs)of the flow problem are rehabilitated into dimensionless forms.The accomplished ordinary differential equations(ODEs)are solved numerically through the bvp4c scheme via a built-in function in computational MATLAB software.The upshots of some prominent physical and bioconvection parameters including wall slip parameters,thermophoresis parameter,Brownian motion parameter,Reiner–Revlin nanofluid parameter,Prandtl number,Peclet number,Lewis number,bioconvection Lewis number,and the mixed convection parameter against velocity,temperature,nanoparticles concentration,and density of motile microorganism profiles are dichotomized and pondered through graphs and tables.The presented computations show that the velocity profiles are de-escalated by the wall slip parameters while the thermal and solutal fields are upgraded with augmentation in thermophoresis number and wall slip parameters.The presence of thermal radiation enhances the temperature profile of nanofluid.The concentration profile of nanoparticles is boosted by intensification in activation energy.Furthermore,the increasing values of bioconvection Lewis number and Peclet number decay the motile microorganisms’field.展开更多
Nowadays multiple wireless communication systems operate in industrial environments side by side.In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network ha...Nowadays multiple wireless communication systems operate in industrial environments side by side.In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold.Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems(iWCS)this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN(general-purpose WLAN)used for non-real time communication.In this paper,we present a Markov chain-based performance model that described the transmission failure of iWCS due to geographical collision with gWLAN.The presented analytic model accurately determines throughput,packet transaction delay,and packet loss probability of iWCS when it is collocated with gWLAN.The results of the Markov model match more than 90%with our simulation results.Furthermore,we proposed an adaptive transmission power control technique for iWCS to overcome the potential interferences caused by the gWLAN transmissions.The simulation results show that the proposed technique significantly improves iWCS performance in terms of throughput,packet transaction,and cycle period reduction.Moreover,it enables the industrial network for the use of delay critical applications in the presence of gWLAN without affecting its performance.展开更多
Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues.The life expectancy of patients diagnosed with gliomas decreases exponentially.Most gliomas are diagnosed in later stages,res...Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues.The life expectancy of patients diagnosed with gliomas decreases exponentially.Most gliomas are diagnosed in later stages,resulting in imminent death.On average,patients do not survive 14 months after diagnosis.The only way to minimize the impact of this inevitable disease is through early diagnosis.The Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain tissues.The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors.Besides this,dealing with MRI scans manually is also cumbersome,thus affects the classification accuracy.To eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification task.Although,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing region.This is because of low variance in enhancing region which give poor contrast in MRI scans.In this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region.Our trained model achieved better results for all glioma classes including the enhancing region.The improved performance of our technique can be attributed to several factors.Firstly,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant noise.Secondly,the architecture we employ can capture the non-linearity of all classes including the enhancing region.Overall,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.展开更多
Kubernetes has become the dominant container orchestration platform,withwidespread adoption across industries.However,its default pod-to-pod communicationmechanism introduces security vulnerabilities,particularly IP s...Kubernetes has become the dominant container orchestration platform,withwidespread adoption across industries.However,its default pod-to-pod communicationmechanism introduces security vulnerabilities,particularly IP spoofing attacks.Attackers can exploit this weakness to impersonate legitimate pods,enabling unauthorized access,lateral movement,and large-scale Distributed Denial of Service(DDoS)attacks.Existing security mechanisms such as network policies and intrusion detection systems introduce latency and performance overhead,making them less effective in dynamic Kubernetes environments.This research presents PodCA,an eBPF-based security framework designed to detect and prevent IP spoofing in real time while minimizing performance impact.PodCA integrates with Kubernetes’Container Network Interface(CNI)and uses eBPF to monitor and validate packet metadata at the kernel level.It maintains a container network mapping table that tracks pod IP assignments,validates packet legitimacy before forwarding,and ensures network integrity.If an attack is detected,PodCA automatically blocks spoofed packets and,in cases of repeated attempts,terminates compromised pods to prevent further exploitation.Experimental evaluation on an AWS Kubernetes cluster demonstrates that PodCA detects and prevents spoofed packets with 100%accuracy.Additionally,resource consumption analysis reveals minimal overhead,with a CPU increase of only 2–3%per node and memory usage rising by 40–60 MB.These results highlight the effectiveness of eBPF in securing Kubernetes environments with low overhead,making it a scalable and efficient security solution for containerized applications.展开更多
This study investigates the key characteristics of compact star configurations within the framework of Rastall’s theory of gravity,employing the Krori-Barua ansatz.By forming the field equations for a spherically sym...This study investigates the key characteristics of compact star configurations within the framework of Rastall’s theory of gravity,employing the Krori-Barua ansatz.By forming the field equations for a spherically symmetric line element with an isotropic matter source through Krori-Barua metric potentials,we derive the modified Tolman-Oppenheimer-Volkov equation.This equation is crucial for studying the mass-radius function,the compactness factor,and the surface redshift.Additionally,we examine various physical aspects,including energy density,pressure evolution,equation of state,adiabatic index,and stability analysis,to assess the model’s viability.Rastall’s theory,which extends general relativity by relaxing the conservation of energy and momentum,plays a central role in our analysis,particularly in understanding the enhanced stability of compact stars.Our results provide strong evidence that within Rastall’s gravitational framework,the proposed stellar structures exhibit significant stability,suggesting that this theory may offer new perspectives on the behavior of such stars.展开更多
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fre...The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.展开更多
For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accur...For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information.To overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages.In the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image fusion.The proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time.The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset.The fusion results of the proposed method are compared with the existing fusion techniques.The quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed method.The proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.展开更多
In this article,we use the prominent Karmarkar condition to investigate some novel features of astronomical objects in the f(R,φ)gravity;R andφrepresent the Ricci curvature and the scalar field,respectively.It is wo...In this article,we use the prominent Karmarkar condition to investigate some novel features of astronomical objects in the f(R,φ)gravity;R andφrepresent the Ricci curvature and the scalar field,respectively.It is worth noting that we classify the exclusive set of modified field equations using the exponential type model of the f(R,φ)theory of gravity f(R,φ)=φ(R+α(eβR-1)).We show the embedded class-I approach via a static,spherically symmetric spacetime with an anisotropic distribution.To accomplish our objective,we use a particular interpretation of metric potential(grr)that has already been given in the literature and then presume the Karmarkar condition to derive the second metric potential.We employ distinct compact stars to determine the values of unknown parameters emerging in metric potentials.To ensure the viability and consistency of our exponential model,we execute distinct physical evolutions,i.e.the graphical structure of energy density and pressure evolution,mass function,adiabatic index,stability,equilibrium,and energy conditions.Our investigation reveals that the observed anisotropic findings are physically appropriate and have the highest level of precision.展开更多
Mobile phones are an essential part of modern life.The two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of people.Among these two,Android curr...Mobile phones are an essential part of modern life.The two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of people.Among these two,Android currently boasts more than 84%market share.Thus,any personal data put on it are at great risk if not properly protected.On the other hand,more than a million pieces of malware have been reported on Android in just 2021 till date.Detecting and mitigating all this malware is extremely difficult for any set of human experts.Due to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this issue.However,deep learning models have primarily been designed for image analysis.While this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of malware.Moreover,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be realized.To resolve this issue,we envision the use of Capsule Networks(CapsNets),a state-of-the-art model in deep learning.We argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of opcodes.We design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep networks.This leads tomuch faster detection as well as increased accuracy.We achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware datasets.Our code is made available as open source and can be used to further enhance our work with minimal effort.展开更多
A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article,to address the attitude tracking control problem of the three...A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article,to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover system.The proposed controller provides convergence of system states in a predetermined finite time and estimates the unmodeled dynamics of the four-rotor system.Dynamic model of the four-rotor system is derived with Newton’s force equations.The unknown dynamics of four-rotor systems are estimated using Radial basis function.The bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence and to provide optimal tracking of the attitude angles while being subjected to unknown dynamics.The global stability proof of the designed controller is provided on the basis of Lyapunov stability theorem.The proposed controller is validated by(i)conducting an experiment through implementing it on the laboratory-based hover system,and(ii)through simulations.Performance of the proposed control scheme is also compared with classical and intelligent controllers.The performance comparison exhibits that the designed controller has quick transient response and improved chattering free steady state performance.The proposed bioinspired global fast terminal sliding mode controller offers improved estimation and better tracking performance than the traditional controllers.In addition,the proposed controller is computationally cost effective and can be implanted on multirotor unmanned air vehicles with limited computational processing capabilities.展开更多
The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing,recommending,prescribing,or treating patie...The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing,recommending,prescribing,or treating patients for uniform phenotype features from patients’profile.The authors of this paper while searching for possible solutions for medical context learning found that unified corpora tagged with medical nomenclature was missing to train the analytics for medical context learning.Therefore,here we demonstrated a mechanism to come up with uniform NER(Named Entity Recognition)tagged medical corpora that is fed with 14407 endocrine patients’data set in Comma Separated Values(CSV)format diagnosed with diabetes mellitus and comorbidity diseases.The other corpus is of ICD-10-CM coding scheme in text format taken from www.icd10data.com.ICD-10-CM corpus is to be tagged for understanding the medical context with uniformity for which we are conducting different experiments using common natural language programming(NLP)techniques and frameworks like TensorFlow,Keras,Long Short-Term Memory(LSTM),and Bi-LSTM.In our preliminary experiments,albeit label sets in form of(instance,label)pair were tagged with Sequential()model formed on TensorFlow.Keras and Bi-LSTM NLP algorithms.The maximum accuracy achieved for model validation was 0.8846.展开更多
The aim of this paper is to investigate modified f(R, ?) theory of gravity, where R and ? represent the Ricci scalar and scalar potential respectively. Specifically, we take the spherically symmetric spacetime to disc...The aim of this paper is to investigate modified f(R, ?) theory of gravity, where R and ? represent the Ricci scalar and scalar potential respectively. Specifically, we take the spherically symmetric spacetime to discuss the possible emergence of compact stars. We study the physical behavior of compact stars by considering 4 U 1820-30, SAX J1808-3658 and Her X1, which are three popular models of compact stars. The graphical analysis of energy density, radial pressure, tangential pressure, energy conditions as well as stability of compact stars has been shown. It is concluded that behavior of these three stars is usual for f(R, ?) gravity models with some specific choices of model parameters.展开更多
In this manuscript,the traversable wormhole solutions have been explored in modified f(R,G)gravity by taking into consideration the model f(R,G)=R+rG2,where R is the Ricci curvature scalar and G is the Gauss–Bonnet t...In this manuscript,the traversable wormhole solutions have been explored in modified f(R,G)gravity by taking into consideration the model f(R,G)=R+rG2,where R is the Ricci curvature scalar and G is the Gauss–Bonnet term.An acceptable form of the redshift function has been incorporated which is a non-constant in nature along with the implementation of those already defined in literature,the two shape functions namely b(r)=r/exp(r-r0)and b(r)=r0log(r+1)/log(r0+1).It is shown by studying the energy condition and through the graphical analysis that the null energy bounds for the effective energy-momentum tensor are generally violated for the presence of the ordinary matter in modified f(R,G)gravity.Energy conditions associated with the matter content threading the wormhole geometries are evaluated and in general are found to favor the null energy conditions in the vicinity of the wormhole throat,the existence of the nonexotic wormhole geometries threaded by the matter has been confirmed under this gravity.展开更多
The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This st...The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.展开更多
Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In th...Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis.Among ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification tasks.The network weights and the activation functions are the two crucial elements in the learning process of an ANN.These weights affect the prediction ability and the convergence efficiency of the network.In traditional settings,ANNs assign random weights to the inputs.This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights.The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer classification.This system computes the confusion matrix-based metrics for traditional and proposed frameworks.The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other models.The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research limitations.Moreover,the proposed framework is of use to predict and classify cancer patients accurately.Consequently,this will facilitate the effective management of cancer patients.展开更多
文摘This article proposes Styrene-Butadiene Rubber(SBR)and Chem-lite CR Powder(CCP)as a sustainable solution for dispersive clays,which cause infrastructure damage due to high sodium ions.Traditionally utilized stabilizers like lime/cement raise environmental concerns due to their high carbon footprints.Regarding this,SBR/CCP has been used in concrete technology for several functions;nevertheless,its effectiveness for stabilizing dispersive clay remains uncertain.Therefore,this study investigated how SBR/CCP improved sodium-rich dispersive soil's dispersion,index,mechanical characteristics,and associated mechanism.Multiple tests,including double hydrometer,cation analysis,compression strength(UCS),physio-chemical,Atterberg's limits,California Bearing Ratio(CBR),X-Ray diffraction(XRD),scanning electron microscopy(SEM),and energy dispersive X-Ray spectroscopy(EDS)were performed at different mixing ratios up to curing of 60-d.The results showed a significant reduction in dispersion(61.7%),sodium(38%),and plasticity(50.4%)with an optimal 1.5%SBR-3%CCP mix after 28-d,converting the clay to a non-dispersive type.UCS and soaked CBR improved by 283%and 579%,respectively.Micro analyses revealed soil enhancement through CCP's flocculation,ion exchange,and pozzolanic reactions,while SBR-coated particles and filled pores formed reticulated membrane systems.SBR/CCP offers a sustainable/eco-friendly alternative for stabilizing dispersive clays with a lower carbon footprint.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
文摘Convective flow is a self-sustained flow with the effect of the temperature gradient.The density is non-uniform due to the variation of temperature.The effect of the magnetic flux plays a major role in convective flow.The process of heat transfer is accompanied by a mass transfer process;for instance,condensation,evaporation,and chemical process.Due to the applications of the heat and mass transfer combined effects in a different field,the main aim of this paper is to do a comprehensive analysis of heat and mass transfer of MHD unsteady second-grade fluid in the presence of ramped boundary conditions near a porous surface.The dynamical analysis of heat transfer is based on classical differentiation with no memory effects.The non-dimensional form of the governing equations of the model is developed.These are solved by the classical integral(Laplace)transform technique/method with the convolution theorem and closed-form solutions are attained for temperature,concentration,and velocity.The physical aspects of distinct parameters are discussed via graph to see the influence on the fluid concentration,velocity,and temperature.Our results suggest that the velocity profile decrease by increasing the Prandtl number.The existence of a Prandtl number may reflect the control of the thickness of momentum and enlargement of thermal conductivity.Furthermore,to validate our results,some results are recovered from the literature.
文摘BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.
基金supported by the Government College University,Faisalabad,and Higher Education Commission,Pakistan.
文摘In current study,the numerical computations of Reiner–Rivlin nanofluid flow through a rotational disk under the influence of thermal radiation and Arrhenius activation energy is considered.For innovative physical situations,the motile microorganisms are incorporated too.The multiple slip effects are considered in the boundary conditions.The bioconvection of motile microorganism is utilized alongside nanofluids to provide stability to enhanced thermal transportation.The Bioconvection pattern in various nanoparticles accredits novel applications of biotechnology like the synthesis of biological polymers,biosensors,fuel cells,petroleum engineering,and the natural environment.By deploying some suitable similarity transformation functions,the governing partial differential equations(PDEs)of the flow problem are rehabilitated into dimensionless forms.The accomplished ordinary differential equations(ODEs)are solved numerically through the bvp4c scheme via a built-in function in computational MATLAB software.The upshots of some prominent physical and bioconvection parameters including wall slip parameters,thermophoresis parameter,Brownian motion parameter,Reiner–Revlin nanofluid parameter,Prandtl number,Peclet number,Lewis number,bioconvection Lewis number,and the mixed convection parameter against velocity,temperature,nanoparticles concentration,and density of motile microorganism profiles are dichotomized and pondered through graphs and tables.The presented computations show that the velocity profiles are de-escalated by the wall slip parameters while the thermal and solutal fields are upgraded with augmentation in thermophoresis number and wall slip parameters.The presence of thermal radiation enhances the temperature profile of nanofluid.The concentration profile of nanoparticles is boosted by intensification in activation energy.Furthermore,the increasing values of bioconvection Lewis number and Peclet number decay the motile microorganisms’field.
基金This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2018R1D1A1B07049758).
文摘Nowadays multiple wireless communication systems operate in industrial environments side by side.In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold.Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems(iWCS)this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN(general-purpose WLAN)used for non-real time communication.In this paper,we present a Markov chain-based performance model that described the transmission failure of iWCS due to geographical collision with gWLAN.The presented analytic model accurately determines throughput,packet transaction delay,and packet loss probability of iWCS when it is collocated with gWLAN.The results of the Markov model match more than 90%with our simulation results.Furthermore,we proposed an adaptive transmission power control technique for iWCS to overcome the potential interferences caused by the gWLAN transmissions.The simulation results show that the proposed technique significantly improves iWCS performance in terms of throughput,packet transaction,and cycle period reduction.Moreover,it enables the industrial network for the use of delay critical applications in the presence of gWLAN without affecting its performance.
文摘Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues.The life expectancy of patients diagnosed with gliomas decreases exponentially.Most gliomas are diagnosed in later stages,resulting in imminent death.On average,patients do not survive 14 months after diagnosis.The only way to minimize the impact of this inevitable disease is through early diagnosis.The Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain tissues.The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors.Besides this,dealing with MRI scans manually is also cumbersome,thus affects the classification accuracy.To eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification task.Although,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing region.This is because of low variance in enhancing region which give poor contrast in MRI scans.In this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region.Our trained model achieved better results for all glioma classes including the enhancing region.The improved performance of our technique can be attributed to several factors.Firstly,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant noise.Secondly,the architecture we employ can capture the non-linearity of all classes including the enhancing region.Overall,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.
基金partially supported by Asia Pacific University of Technology&Innovation(APU)Bukit Jalil,Kuala Lumpur,MalaysiaThe funding body had no role in the study design,data collection,analysis,interpretation,or writing of the manuscript.
文摘Kubernetes has become the dominant container orchestration platform,withwidespread adoption across industries.However,its default pod-to-pod communicationmechanism introduces security vulnerabilities,particularly IP spoofing attacks.Attackers can exploit this weakness to impersonate legitimate pods,enabling unauthorized access,lateral movement,and large-scale Distributed Denial of Service(DDoS)attacks.Existing security mechanisms such as network policies and intrusion detection systems introduce latency and performance overhead,making them less effective in dynamic Kubernetes environments.This research presents PodCA,an eBPF-based security framework designed to detect and prevent IP spoofing in real time while minimizing performance impact.PodCA integrates with Kubernetes’Container Network Interface(CNI)and uses eBPF to monitor and validate packet metadata at the kernel level.It maintains a container network mapping table that tracks pod IP assignments,validates packet legitimacy before forwarding,and ensures network integrity.If an attack is detected,PodCA automatically blocks spoofed packets and,in cases of repeated attempts,terminates compromised pods to prevent further exploitation.Experimental evaluation on an AWS Kubernetes cluster demonstrates that PodCA detects and prevents spoofed packets with 100%accuracy.Additionally,resource consumption analysis reveals minimal overhead,with a CPU increase of only 2–3%per node and memory usage rising by 40–60 MB.These results highlight the effectiveness of eBPF in securing Kubernetes environments with low overhead,making it a scalable and efficient security solution for containerized applications.
基金The author,FM,expresses her gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project No.(PNURSP2025R27),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘This study investigates the key characteristics of compact star configurations within the framework of Rastall’s theory of gravity,employing the Krori-Barua ansatz.By forming the field equations for a spherically symmetric line element with an isotropic matter source through Krori-Barua metric potentials,we derive the modified Tolman-Oppenheimer-Volkov equation.This equation is crucial for studying the mass-radius function,the compactness factor,and the surface redshift.Additionally,we examine various physical aspects,including energy density,pressure evolution,equation of state,adiabatic index,and stability analysis,to assess the model’s viability.Rastall’s theory,which extends general relativity by relaxing the conservation of energy and momentum,plays a central role in our analysis,particularly in understanding the enhanced stability of compact stars.Our results provide strong evidence that within Rastall’s gravitational framework,the proposed stellar structures exhibit significant stability,suggesting that this theory may offer new perspectives on the behavior of such stars.
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
文摘The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.
文摘For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information.To overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages.In the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image fusion.The proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time.The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset.The fusion results of the proposed method are compared with the existing fusion techniques.The quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed method.The proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.
基金the Grant No.YS304023912 to support his Postdoctoral Fellowship at Zhejiang Normal University,ChinaPrincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R27),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘In this article,we use the prominent Karmarkar condition to investigate some novel features of astronomical objects in the f(R,φ)gravity;R andφrepresent the Ricci curvature and the scalar field,respectively.It is worth noting that we classify the exclusive set of modified field equations using the exponential type model of the f(R,φ)theory of gravity f(R,φ)=φ(R+α(eβR-1)).We show the embedded class-I approach via a static,spherically symmetric spacetime with an anisotropic distribution.To accomplish our objective,we use a particular interpretation of metric potential(grr)that has already been given in the literature and then presume the Karmarkar condition to derive the second metric potential.We employ distinct compact stars to determine the values of unknown parameters emerging in metric potentials.To ensure the viability and consistency of our exponential model,we execute distinct physical evolutions,i.e.the graphical structure of energy density and pressure evolution,mass function,adiabatic index,stability,equilibrium,and energy conditions.Our investigation reveals that the observed anisotropic findings are physically appropriate and have the highest level of precision.
文摘Mobile phones are an essential part of modern life.The two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of people.Among these two,Android currently boasts more than 84%market share.Thus,any personal data put on it are at great risk if not properly protected.On the other hand,more than a million pieces of malware have been reported on Android in just 2021 till date.Detecting and mitigating all this malware is extremely difficult for any set of human experts.Due to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this issue.However,deep learning models have primarily been designed for image analysis.While this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of malware.Moreover,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be realized.To resolve this issue,we envision the use of Capsule Networks(CapsNets),a state-of-the-art model in deep learning.We argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of opcodes.We design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep networks.This leads tomuch faster detection as well as increased accuracy.We achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware datasets.Our code is made available as open source and can be used to further enhance our work with minimal effort.
文摘A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article,to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover system.The proposed controller provides convergence of system states in a predetermined finite time and estimates the unmodeled dynamics of the four-rotor system.Dynamic model of the four-rotor system is derived with Newton’s force equations.The unknown dynamics of four-rotor systems are estimated using Radial basis function.The bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence and to provide optimal tracking of the attitude angles while being subjected to unknown dynamics.The global stability proof of the designed controller is provided on the basis of Lyapunov stability theorem.The proposed controller is validated by(i)conducting an experiment through implementing it on the laboratory-based hover system,and(ii)through simulations.Performance of the proposed control scheme is also compared with classical and intelligent controllers.The performance comparison exhibits that the designed controller has quick transient response and improved chattering free steady state performance.The proposed bioinspired global fast terminal sliding mode controller offers improved estimation and better tracking performance than the traditional controllers.In addition,the proposed controller is computationally cost effective and can be implanted on multirotor unmanned air vehicles with limited computational processing capabilities.
基金This research is supported by Shifa International Hospital,Pakistan.Endocrine patients’data contributed for diagnosis of diabetes,and its comorbidities holds a lot of worth to come up with these observations from experimental study。
文摘The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing,recommending,prescribing,or treating patients for uniform phenotype features from patients’profile.The authors of this paper while searching for possible solutions for medical context learning found that unified corpora tagged with medical nomenclature was missing to train the analytics for medical context learning.Therefore,here we demonstrated a mechanism to come up with uniform NER(Named Entity Recognition)tagged medical corpora that is fed with 14407 endocrine patients’data set in Comma Separated Values(CSV)format diagnosed with diabetes mellitus and comorbidity diseases.The other corpus is of ICD-10-CM coding scheme in text format taken from www.icd10data.com.ICD-10-CM corpus is to be tagged for understanding the medical context with uniformity for which we are conducting different experiments using common natural language programming(NLP)techniques and frameworks like TensorFlow,Keras,Long Short-Term Memory(LSTM),and Bi-LSTM.In our preliminary experiments,albeit label sets in form of(instance,label)pair were tagged with Sequential()model formed on TensorFlow.Keras and Bi-LSTM NLP algorithms.The maximum accuracy achieved for model validation was 0.8846.
基金National University of Computer and Emerging Sciences(NUCES),Pakistan
文摘The aim of this paper is to investigate modified f(R, ?) theory of gravity, where R and ? represent the Ricci scalar and scalar potential respectively. Specifically, we take the spherically symmetric spacetime to discuss the possible emergence of compact stars. We study the physical behavior of compact stars by considering 4 U 1820-30, SAX J1808-3658 and Her X1, which are three popular models of compact stars. The graphical analysis of energy density, radial pressure, tangential pressure, energy conditions as well as stability of compact stars has been shown. It is concluded that behavior of these three stars is usual for f(R, ?) gravity models with some specific choices of model parameters.
文摘In this manuscript,the traversable wormhole solutions have been explored in modified f(R,G)gravity by taking into consideration the model f(R,G)=R+rG2,where R is the Ricci curvature scalar and G is the Gauss–Bonnet term.An acceptable form of the redshift function has been incorporated which is a non-constant in nature along with the implementation of those already defined in literature,the two shape functions namely b(r)=r/exp(r-r0)and b(r)=r0log(r+1)/log(r0+1).It is shown by studying the energy condition and through the graphical analysis that the null energy bounds for the effective energy-momentum tensor are generally violated for the presence of the ordinary matter in modified f(R,G)gravity.Energy conditions associated with the matter content threading the wormhole geometries are evaluated and in general are found to favor the null energy conditions in the vicinity of the wormhole throat,the existence of the nonexotic wormhole geometries threaded by the matter has been confirmed under this gravity.
文摘The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.
文摘Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis.Among ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification tasks.The network weights and the activation functions are the two crucial elements in the learning process of an ANN.These weights affect the prediction ability and the convergence efficiency of the network.In traditional settings,ANNs assign random weights to the inputs.This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights.The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer classification.This system computes the confusion matrix-based metrics for traditional and proposed frameworks.The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other models.The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research limitations.Moreover,the proposed framework is of use to predict and classify cancer patients accurately.Consequently,this will facilitate the effective management of cancer patients.
